Autonomous AI Tutors. Humanlike Learning, Without the Overhead.
From any question to a reproducible, evidence-based answer—with provenance, bias checks, and one-click hand-offs to your tools.
From any question to a reproducible, evidence-based answer—with provenance, bias checks, and one-click hand-offs to your tools.
Plans and runs reproducible research
Executes roles, maintains snapshots
Individual research results and status
Hashes, inputs and outputs
Sign-off, citations, provenance history
Reuseable findings and procedures
SuperLabs orchestrates a multi-agent research team that’s tuned with vertical AI adapters for your domain. Agents plan, debate, and execute protocols; vertical adapters provide tax-specific schemas, rules, and sources—so results are reproducible and defensible.
Question (Clarifier): “Which SST exemptions apply to digital services for SMEs in Malaysia for FY2025?”
Scout: Retrieve MOF/LHDN/SST circulars, budget speeches, and gazettes; collect prior interpretations.
Hypothesizer: Frame testable hypotheses (e.g., “SME digital SaaS with local consumption qualifies for X exemption under Section Y”).
Methodologist: Build a protocol: source list, citation validity checks, applicability tests (entity size, place of supply), and stop-rules.
Experimentalist/Causalist: Run rule evaluators against example invoices and entity profiles; check confounders (cross-border, reverse charge).
Critic/Replicator: Red-team assumptions (edge cases like marketplace facilitators) and replicate with alternate sources.
Malaysia Taxation (worked example)
Validated
Question (Clarifier): “Which SST exemptions apply to digital services for SMEs in Malaysia for FY2025?”
Received: 78lg&I54hf
From any question to a reproducible, evidence-based answer -with provenance, bias checks and one-click hands-off to your tools.
SuperLabs interprets intent and assembles a protocol; the Orchestrator assigns agents by role.
Provenance by default
Every step hashed and linked
Versioned everything
Data, protocols, features and claims
Bias & safety gates
Agents red-team assumptions before publish
Environments
Dev → Stage → Prod with promotion gates
Modules you will see
Question, hypothesis, claim, linked evidence
Agents execute experiments-as-code with snapshots, retries, and guardrails.
Agents run steps
steps end-to-end with resource limits, timeouts, and parallelism.
Inputs locked
(data + params snapshot) for exact reproducibility.
Scheduling & retries
with jitter/backoff; resume from failed step.
Safeguards
PII rules, leakage checks, and bias gates before publish.
Provenance ledger
hash every step; link to the Research Card.
Charts + correlations linked to the Evidence Board; confidence and assumptions are explicit.
Charts & summeries
Evidence links
Confidence & assumptions
Export & share
Variants & cohorts
Bias & safety gates pass; provenance chain complete; status set (Exploratory → …).
Bias & safely gates
Provenance hasing
Epistemic status
Repro pack
Approval & sync
Bias checks passed
No leakage
Provenance ledger complete
Status set
Approvals recorded
Publish Research Cards/Dossiers/Repro Packs; APIs/webhooks handle downstream updates with optional human review.
Continuous ingestion from documents, data lakes, and the web.
From warehouses and ELNs/LIMS to CRMs, wikis, and chat.
Every Research Card and protocol step is hashed, versioned, and auditable.
Hands-on help across time zones.
How does Evidence Harmonization work?
Can I review/override field mappings?
What gets synced back to my tools?
Turn scattered evidence into confident action. SuperLabs harmonizes sources, runs reproducible protocols in plain language, and syncs validated outcomes back to the tools you already use.
Every step is hashed and versioned—nothing opaque.
Clear labels (Exploratory → Validated → Replicated) on every claim.
Agents red-team sources and assumptions before results ship.
Granular roles, review trails, and air-gapped workflows when required.
SuperLabs turns messy data into traceable, bias-checked results with explicit epistemic status, ready to ship to your tools.
SuperLabsSuperLabs is the evidence OS for research-driven teams—harmonizing sources, running reproducible protocols, labeling epistemic status, and syncing validated outcomes back to the tools you already use.